To train the pedestrian detector module, we use many popular publicly available pedestrian detection datasets: MOT17Det [1]}, MOT20Det [2]}, CityPersons [3]}, CrowdHuman [4]}, PRW [5]} and CUHK-SYSU [6]}. These datasets are described in sec:back:datasets. We exploit also viped, a synthetic dataset introduced in ch:virtual-to-real. Furthermore, we introduce a novel dataset that we collected and annotated on purpose, named CrowdVisorPisa, also employed for evaluating our solution. In particular, it comprises 15 different sequences gathered from a webcam located in a public square of the city of Pisa, Italy, each of which consists of ten images captured with a time interval of 1 second. We manually labeled all frames, localizing the pedestrian instances with bounding boxes. More, we also annotated each sequence taking track of the different pedestrian entities entering or exiting the scene. We divided the dataset into train and test splits, considering 10 and 5 different sequences, respectively. The former split is exploited to train the object detector module, while the latter is used to evaluate the performance of some modules of our framework. It is worth noting that, due to camera positioning not modifiable for local restrictions, this dataset represents a particularly challenging scenario as people instances are small and sometimes difficult to localize. A sample of the dataset is shown in fig:crowdvisorpisasample.
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